DeepMeaning builds efficient, personalized and transparent solutions to facilitate the implementation
of your digital strategy and to explore your data.
Our expertise in Data Architecture, Data Science, Big Data, Data Visualization allows us to construct your Data Centric Platforms
Our R&D challenge is to develop Artificial Intelligence approach in different fields: medical diagnostics, seismology, climatology, energetics.
We want to help people to derive knowledge from data by using advanced algorithmic research, by creating our own algorithms, by applying our expertise of Big Data analytics and mathematical approach.
Machine Learning and Deep Learning allow to analyze vast amount of data, automatically detect patterns and make accurate predictions. These models proved to be effective in such fields as finance, CRM, communication, computer vision, natural language processing and many others.
Deep Meaning puts the data in the center of Information System Strategy. Our Data Lab builds customized solutions for the implementation of your Data Centric Architecture in order to make it easier for you to explore your data.
Our team is focused on constructing appropriate models, improving our algorithms according to the latest research and training these algorithms on large data set.
To increase the capacity of your data we concentrate on:
Our R&D team develops tools to enable the use of Artificial Intelligence in diagnosis of cardiac abnormalities.
We are applying our multidisciplinary approach by combining Physics, Mathematics, Medicine, Machine Learning and large scale computing systems.
Our goal is to build solutions to deliver diagnostics that is more precise, accurate and useful for doctors, to increase quality and optimize time and cost of analysis, to deliver solutions for more personalized treatment for majority of patients.
We develop algorithms for understanding cardiac electrical activity and detecting abnormal heart activity.
This field operates at the intersection of Cardiology, Physics, Computer Science and Egineering.
We develop new algorithms for inverting ill-posed matrices.
Our approach based on development of iterative schemes, in particular on further development of Gauss–Seidel method. The improvement achieves by eliminating division and introducing of several parameters follow up by optimization. The suggested technology can be applied to stabilize various numeric schemes, to detecting equilibriums and cycles in biological models, in particular to model Allee effect phenomena.
Go to MATRIX project page